code: def __init__(
      self,
      in_channel=96,
      out_channel=96,
      kernel_size=3,
      stride=1,
      groups=1,
      act="leaky_relu",
  ):
      super(ConvBNLayer, self).__init__()
      initializer = nn.initializer.KaimingUniform()
      self.act = act
      assert self.act in ["leaky_relu", "hard_swish"]
      self.conv = nn.Conv2D(
          in_channels=in_channel,
          out_channels=out_channel,
          kernel_size=kernel_size,
          groups=groups,
          padding=(kernel_size - 1) // 2,
          stride=stride,
          weight_attr=ParamAttr(initializer=initializer),
          bias_attr=False,
      )
      self.bn = nn.BatchNorm2D(out_channel)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
label_desc: Use image node_id 1 to create a new ConvBNLayer object from inherited base classes: nn with example: obj = ConvBNLayer(in_channel, out_channel, kernel_size, stride, groups, act)